WebsiteData LabelingFreemium

What Is Defined.ai (formerly Figure Eight) Used For: Features, Reviews & Alternatives

Provides AI training data and labeling tools.

Editorially updated Oct 25, 2025

The overview

What Defined.ai (formerly Figure Eight) is for

Defined.ai provides a web-based platform for defining, executing, and managing human-in-the-loop data annotation projects. It enables AI development teams to generate high-quality ground truth datasets across various data modalities directly through a browser interface, facilitating the training and validation of machine learning models without requiring extensive in-house annotation infrastructure.

Key features

1Core Capabilitie

Multi-modal data ingestion interface
Customizable annotation tool palette (e.g., bounding box, polygon, keypoint, transcription)
Real-time quality control dashboard
Project progress and throughput monitoring

2Specialized Workflow

Granular instruction set editor for annotator guideline
Inter-annotator agreement (IAA) configuration and reporting
Crowdsourcing workforce management panel
API-driven data export endpoint for labeled dataset

Who it helps

Useful ways to use Defined.ai (formerly Figure Eight)

Accelerating Model Training with Ground Truth Data

ML engineers leverage the platform to generate high-fidelity labeled datasets for supervised learning, reducing model bias and improving generalization. This includes tasks like object detection, semantic segmentation, natural language understanding, and audio transcription

Scaling Annotation Project Management

AI project managers utilize the web interface to oversee large-scale data labeling initiatives, manage annotator pools, and enforce strict quality assurance protocols across diverse data modalities, ensuring timely delivery of production-ready dataset

Rapid Dataset Prototyping for AI MVP

Early-stage AI product teams quickly define and execute labeling tasks to build initial training datasets, enabling faster iteration and validation of core AI features without extensive in-house annotation infrastructure or hiring dedicated annotator

A practical path

How to use Defined.ai (formerly Figure Eight)

Access Project Creation Interface

Navigate to the project creation section, select the relevant data modality (e.g., image, text, audio), and upload raw data files or connect a data source via API or cloud storage integration

External signals

Reviews & reputation

AI aggregated

3.4/ 5

Aggregated review score

A robust, web-based platform highly valued by AI teams for its comprehensive data labeling tools, flexible workforce management, and strong quality assurance features. Users appreciate its ability to handle diverse data modalities and scale annotation projects efficiently, though some note the learning curve for advanced configurations.

G2 LarryLudwig Product Review SmartCustomer Trustpilot

Quick answers

Frequently asked questions

1What quality control mechanisms are in place to ensure annotation accuracy for complex tasks?⌄

The platform employs multiple quality control layers, including golden datasets (ground truth tasks), inter-annotator agreement (IAA) metrics, majority vote consensus, and human review stages. Project managers can configure these mechanisms to suit the specific accuracy requirements of their annotation tasks and data modalities.

2How does the pricing model work for different project scales and data types?⌄

Pricing is typically based on a per-task or per-unit basis, varying by data modality (e.g., per image, per minute of audio, per text unit) and task complexity. Volume discounts are often available for larger projects, and custom enterprise plans can be negotiated for specific throughput or dedicated workforce needs.

3What data security and privacy measures protect sensitive information uploaded for labeling?⌄

The platform adheres to industry-standard security protocols, including data encryption in transit and at rest, access controls, and compliance certifications (e.g., GDPR, HIPAA readiness). Data anonymization features and secure project environments are available to protect sensitive client data during the annotation process.

4Can I integrate my existing data pipelines with the platform for automated data ingestion and export?⌄

Yes, the platform provides robust API endpoints for programmatic data ingestion, task creation, and labeled data export. This allows for seamless integration with existing MLOps pipelines, cloud storage solutions, and custom data management systems, automating the flow of data to and from the annotation platform.

5What types of data modalities and annotation tasks does the platform support?⌄

The platform supports a wide range of data modalities including images (object detection, segmentation, classification), video (tracking, event detection), text (sentiment analysis, named entity recognition, summarization), audio (transcription, speaker diarization), and tabular data. Custom task types can also be configured for unique project requirements.

Keep exploring

More products

Browse all websites